In modern digital systems, digital information has to be processed in a reliable and efficient way. In this context, digital information is to be understood as information available in discrete, i.e., discontinuous values. Bits, collection of bits, but also numbers from a finite set can be used to represent digital information.
In most chip-to-chip, or device-to-device communication systems, communication takes place over a plurality of wires to increase the aggregate bandwidth. A single or pair of these wires may be referred to as a channel or link and multiple channels create a communication bus between the electronic components. At the physical circuitry level, in chip-to-chip communication systems, buses are typically made of electrical conductors in the package between chips and motherboards, on printed circuit boards (“PCBs”) boards or in cables and connectors between PCBs. In high frequency applications, microstrip or stripline PCB traces may be used.
Common methods for transmitting signals over bus wires include single-ended and differential signaling methods. In applications requiring high speed communications, those methods can be further optimized in terms of power consumption and pin-efficiency, especially in high-speed communications. More recently, vector signaling methods such as described in [Shokrollahi] have been proposed to further optimize the trade-offs between power consumption, pin efficiency and noise robustness of chip-to-chip communication systems. In those vector signaling systems, digital information at the transmitter is transformed into a different representation space in the form of a vector codeword that is chosen in order to optimize the power consumption, pin-efficiency and speed trade-offs based on the transmission channel properties and communication system design constraints. Herein, this process is referred to as “encoding”. The encoded codeword is communicated as a group of signals from the transmitter to one or more receivers. At a receiver, the received signals corresponding to the codeword are transformed back into the original digital information representation space. Herein, this process is referred to as “decoding”.
Regardless of the encoding method used, the received signals presented to the receiving device are sampled (or their signal value otherwise recorded) at intervals best representing the original transmitted values, regardless of transmission channel delays, interference, and noise. This Clock and Data Recovery (CDR) not only must determine the appropriate sample timing, but must continue to do so continuously, providing dynamic compensation for varying signal propagation conditions. It is common for communications receivers to extract a receive clock signal from the received data stream. Some communications protocols facilitate such Clock Data Recovery or CDR operation by constraining the communications signaling so as to distinguish between clock-related and data-related signal components.
Similarly, some communications receivers process the received signals beyond the minimum necessary to detect data, so as to provide the additional information to facilitate clock recovery. As one example, a so-called double-baud-rate receive sampler may measure received signal levels at twice the expected data reception rate, to allow independent detection of the received signal level corresponding to the data component, and the chronologically offset received signal transition related to the signal clock component. However, the introduction of extraneous communications protocol transitions is known to limit achievable data communication rate. Similarly, receive sampling at higher than transmitted data rate is known to substantially increase receiver power utilization.
Real-world communications channels are imperfect, degrading transmitted signals in both amplitude (e.g. attenuation) and timing (e.g. delay and pulse smearing) which may be addressed via transmitter pre-compensation and/or receive equalization. Continuous time linear equalization (CTLE) is one known approach to frequency domain equalization, in one example providing compensation for increased channel attenuation at high frequencies. Time-domain-oriented equalization methods are also used to compensate for the effects of inter-symbol-interference or ISI on the received signal. Such ISI is caused by the residual electrical effects of a previously transmitted signal persisting in the communications transmission medium, so as to affect the amplitude or timing of the current symbol interval. As one example, a transmission line medium having one or more impedance anomalies may introduce signal reflections. Thus, a transmitted signal will propagate over the medium and be partially reflected by one or more such anomalies, with such reflections appearing at the receiver at a later time in superposition with signals propagating directly.
One method of data-dependent receive equalization is Decision Feedback Equalization or DFE. Here, the time-domain oriented equalization is performed by maintaining a history of previously-received data values at the receiver, which are processed by a transmission line model to predict the expected influence that each of the historical data values would have on the present receive signal. Such a transmission line model may be pre-calculated, derived by measurement, or generated heuristically, and may encompass the effects of one or more than one previous data interval. The predicted influence of these one or more previous data intervals is collectively called the DFE compensation. At low to moderate data rates, the DFE compensation may be calculated in time to be applied before the next data sample is detected, as example by being explicitly subtracted from the received data signal prior to receive sampling, or implicitly subtracted by modifying the reference level to which the received data signal is compared in the receive data sampler or comparator. However, at higher data rates the detection of previous data bits and computation of the DFE compensation may not be complete in time for the next data sample, requiring use of so-called “unrolled” DFE computations performed on speculative or potential data values rather than known previous data values. As one example, an unrolled DFE stage may predict two different compensation values depending on whether the determining data bit will resolve to a one or a zero, with the receive detector performing sampling or slicing operations based on each of those predictions, the multiple results being maintained until the DFE decision is resolved.